使用多目标灰狼优化算法进行直流电阻率与MT数据的联合反演
Joint Inversion of DC Resistivity and MT Data using Multi-Objective Grey Wolf Optimization
AI总结：
本文提出采用多目标灰狼优化算法MOGWO联合反演直流电阻率与MT数据，通过Broken Hill现场案例验证其可获得可靠的电阻率剖面并评估参数不确定性。
摘要：
地球物理数据集的联合反演通过利用多种地球物理方法的信息，在地下表征中具有重要作用，并已获得广泛关注。在本研究中，我们使用多目标灰狼优化（Multi-Objective Grey Wolf Optimization，MOGWO）算法实现了直流电阻率与MT数据的联合反演。作为广泛使用的灰狼优化算法的扩展，MOGWO提供了一组Pareto最优非支配解，从而无需在目标函数中设置权重参数。这组非支配预测还有助于理解所预测模型参数中的不确定性。通过在澳大利亚中南部Broken Hill周边地区开展的现场案例研究，本文展示了MOGWO在联合反演方面的能力；解集合中狭窄的分布范围表明，其能够对模型参数（电阻率剖面）给出可信估计。所得结果与成熟方法相当，并突显了MOGWO作为地球物理勘探中可靠工具的有效性。
英文摘要：
Joint inversion of geophysical datasets is instrumental in subsurface characterization and has garnered significant popularity, leveraging information from multiple geophysical methods. In this study, we implemented the joint inversion of DC resistivity with MT data using the Multi-Objective Grey Wolf Optimization (MOGWO) algorithm. As an extension of the widely-used Grey Wolf Optimization algorithm, MOGWO offers a suite of pareto optimal non-dominated solutions, eliminating the need for weighting parameters in the objective functions. This set of non-dominated predictions also facilitates the understanding of uncertainty in the predicted model parameters. Through a field case study in the region around Broken Hill in South Central Australia, the paper showcases MOGWO's capabilities in joint inversion, providing confident estimates of the model parameters (resistivity profiles), as indicated by a narrow spread in the suite of solutions. The obtained results are comparable to well established methodologies and highlight the efficacy of MOGWO as a reliable tool in geophysical exploration.
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